Pier Luigi Martelli

MultifacetedProtDB is a database of multifunctional human proteins deriving information from other databases, including UniProt, GeneCards, Human Protein Atlas (HPA), Human Phenotype Ontology (HPO) and MONDO. It collects under the label ‘multifaceted’ multitasking proteins addressed in literature as pleiotropic, multidomain, promiscuous (in relation to enzymes catalysing multiple substrates) and moonlighting (with two or more molecular functions), and difficult to be retrieved with a direct search in existing non-specific databases. The study of multifunctional proteins is an expanding research area aiming to elucidate the complexities of biological processes, particularly in humans, where multifunctional proteins play roles in various processes, including signal transduction, metabolism, gene regulation and cellular communication, and are often involved in disease insurgence and progression. The webserver allows searching by gene, protein and any associated structural and functional information, like available structures from PDB, structural models and interactors, using multiple filters. Protein entries are supplemented with comprehensive annotations including EC number, GO terms (biological pathways, molecular functions, and cellular components), pathways from Reactome, subcellular localization from UniProt, tissue and cell type expression from HPA, and associated diseases following MONDO, Orphanet and OMIM classification. MultiFacetedProtDB is freely available as a web server at: https://multifacetedprotdb.biocomp.unibo.it/.

Proteins are “social molecules.” Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein‐aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein‐binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never‐seen‐before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML‐computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind data sets. Results indicate that the state‐of‐the‐art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI‐functional interactions. This article is categorized under: Software > Molecular Modeling Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Interactions